Transformer Network-based Image Segmentation using Hybrid Flower Pollination Optimization

Effective image segmentation is important in various fields, from medical imaging to autonomous vehicles. This paper presents an integration of Hybrid Flower Pollination Optimization (HFPO) with Transformer networks, considering strengths of global optimization and deep learning. HFPO, inspired by natural pollination processes, excels in exploring vast solution spaces, while Transformers, renowned in deep learning, capture complex patterns. This fusion enhances segmentation accuracy and computational efficiency. Balancing sophistication and computational feasibility, this approach marks a significant advancement in image analysis, promising transformative applications across industries. The experimentation and evaluation, reveal that the proposed HFPO-Transformer based model for image segmentation is more efficient than the existing methods.